Development of a conflict risk evaluation model to assess pedestrian safety in interaction with vehicles

Amini, Roja Ezzati; Yang, Kui; Antoniou, Constantinos

doi:10.1016/j.aap.2022.106773

Cited by 10 publications

(3 citation statements)

References 41 publications

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“…This choice was made with practical applications in mind, where measures relatively easy to compute, yet reliable, need to be used. It should be noted that more advanced approaches exist in the literature, for example combining information from several surrogate measures of safety (Ezzati Amini et al, 2022). More details on TTAC calculation are given in Section 3.2.…”

Section: Methodological Frameworkmentioning

confidence: 99%

Before-after safety analysis of a shared space implementation

Orsini

Batista

Friedrich

et al. 2023

Case Studies on Transport Policy

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Section: Methodological Frameworkmentioning

confidence: 99%

Before-after safety analysis of a shared space implementation

Orsini

Batista

Friedrich

et al. 2023

Case Studies on Transport Policy

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“…Current intersection risk research can be divided into two directions. Some studies focus on individual intersections, exploring risk distribution within the road network [1], but overlook internal risk variations. Through-lanes are found to pose a higher risk of severe pedestrian injuries [2] .…”

Section: Introductionmentioning

confidence: 99%

Risk assessment, prediction, and factor analysis for lanes at urban intersection

Wang,

Xu,

et al. 2024

International Conference on Smart Transportation and City Engineering (STCE 2023)

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To determine the risk levels of urban signal-controlled intersection lanes, we propose two comprehensive risk assessment sets that incorporate time proximity, spatial proximity, and velocity-based indicators. Using actual trajectory data of intersection users, we analyse the interactions between three common vulnerable road users and motor vehicles. Bayesian inference algorithm is employed to evaluate lane risk levels. Additionally, XGBoost, random algorithm, and SVM algorithm with a Gaussian kernel are utilized to predict intersection lane risk. The importance and correlation of risk factors are explored using SHAP value theory. Results indicate that while extremely dangerous collisions are less likely due to signal control, moderate risk remains prevalent. The XGBoost model outperforms the other methods with an accuracy rate of 91.51% and precision rate of 94.17%. Among the analysed interaction risk factors, the speed and acceleration of road users have the greatest impact on lane risk levels. In low traffic flow conditions, higher speeds of motorcycles and cars increase collision risks. Lanes three and four exhibit higher probabilities of hazardous interactions between pedestrians and cars, resulting in more severe pedestrian injuries. Additionally, motorcycles at lower speeds are more prone to risky interactions with pedestrians.

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“…It is not surprising that pedestrian conflicts with vehicles are most problematic in urban areas, since pedestrian activity is higher there. The problem of collisions between vehicles and pedestrians has been the subject of deep study for a long time [5][6][7][8][9][10][11][12]. The meaning of traffic conflict varies among research publications.…”

Section: Introductionmentioning

confidence: 99%

Holistic Spatio-Temporal Graph Attention for Trajectory Prediction in Vehicle–Pedestrian Interactions

Alghodhaifi,

Lakshmanan

2023

Sensors

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Ensuring that intelligent vehicles do not cause fatal collisions remains a persistent challenge due to pedestrians’ unpredictable movements and behavior. The potential for risky situations or collisions arising from even minor misunderstandings in vehicle–pedestrian interactions is a cause for great concern. Considerable research has been dedicated to the advancement of predictive models for pedestrian behavior through trajectory prediction, as well as the exploration of the intricate dynamics of vehicle–pedestrian interactions. However, it is important to note that these studies have certain limitations. In this paper, we propose a novel graph-based trajectory prediction model for vehicle–pedestrian interactions called Holistic Spatio-Temporal Graph Attention (HSTGA) to address these limitations. HSTGA first extracts vehicle–pedestrian interaction spatial features using a multi-layer perceptron (MLP) sub-network and max pooling. Then, the vehicle–pedestrian interaction features are aggregated with the spatial features of pedestrians and vehicles to be fed into the LSTM. The LSTM is modified to learn the vehicle–pedestrian interactions adaptively. Moreover, HSTGA models temporal interactions using an additional LSTM. Then, it models the spatial interactions among pedestrians and between pedestrians and vehicles using graph attention networks (GATs) to combine the hidden states of the LSTMs. We evaluate the performance of HSTGA on three different scenario datasets, including complex unsignalized roundabouts with no crosswalks and unsignalized intersections. The results show that HSTGA outperforms several state-of-the-art methods in predicting linear, curvilinear, and piece-wise linear trajectories of vehicles and pedestrians. Our approach provides a more comprehensive understanding of social interactions, enabling more accurate trajectory prediction for safe vehicle navigation.

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Development of a conflict risk evaluation model to assess pedestrian safety in interaction with vehicles

Cited by 10 publications

References 41 publications

Before-after safety analysis of a shared space implementation

Before-after safety analysis of a shared space implementation

Risk assessment, prediction, and factor analysis for lanes at urban intersection

Holistic Spatio-Temporal Graph Attention for Trajectory Prediction in Vehicle–Pedestrian Interactions

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